Abstract

The combination of multisource remote sensing and geographic data is believed to offer improved accuracies in land cover classification. For such classification, the conventional parametric statistical classifiers, which have been applied successfully in remote sensing for the last two decades, are not appropriate, since a convenient multivariate statistical model does not exist for the data. In this paper, several single and multiple classifiers, that are appropriate for the classification of multisource remote sensing and geographic data are considered. The focus is on multiple classifiers: bagging algorithms, boosting algorithms, and consensus-theoretic classifiers. These multiple classifiers have different characteristics. The performance of the algorithms in terms of accuracies is compared for two multisource remote sensing and geographic datasets. In the experiments, the multiple classifiers outperform the single classifiers in terms of overall accuracies.

Keywords

Computer scienceBoosting (machine learning)Land coverArtificial intelligenceStatistical classificationRandom subspace methodData miningRemote sensingRemote sensing applicationMachine learningParametric statisticsClassifier (UML)Pattern recognition (psychology)GeographyHyperspectral imagingLand useMathematics

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Publication Info

Year
2002
Type
article
Volume
40
Issue
10
Pages
2291-2299
Citations
294
Access
Closed

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G.J. Briem, Jón Atli Benediktsson, Jóhannes R. Sveinsson (2002). Multiple classifiers applied to multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing , 40 (10) , 2291-2299. https://doi.org/10.1109/tgrs.2002.802476

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DOI
10.1109/tgrs.2002.802476